Foreground object detection is a process to separate foreground objects from the background in images captured from a video camera. Foreground object detection has various applications, such as video surveillance or object-based video coding. Methods being practiced for foreground object detection are generally based on background subtraction with the assumption that cameras are stationary and a background model can be created and updated over time. There are several popular techniques being practiced in the field, including adaptive Mixture of Gaussian (MOG), Kernel Density Estimation (KDE) and Codebook methods. All these foreground/background detection methods utilize image processing techniques to process color/intensity images captured by a video camera, where the captured images do not contain depth information. The above color/intensity-based techniques usually involve high computational complexity and the detection result may not be satisfactory. A method based on Mixture of Gaussian has been applied to images with combined depth and color information. Nevertheless, the MOG based approach simply treats the depth information as a color component and results in even higher computational complexity. Accordingly, it is desirable to develop low-complexity foreground object detection method and apparatus that utilize the depth information from depth images to detect foreground/background in the scene associated with the depth images.